Master Thesis project - Applied mathematics, Statistics, Computer science - RISE, Sweden

We use both statistical methods and machine learning to understand the relationship between the structure/geometry of porous materials and their mass transport properties, i.e. diffusive transport and fluid flow. In a recent project, we generated a large number of virtual materials structures and computed diffusivity and fluid permeability using lattice Boltzmann methods. The data set consists of 90,000 binary 3D arrays of size 192 3 and the corresponding computed properties, to our knowledge the largest dataset ever of this kind. We used both artificial neural networks (ANNs) and 3D convolutional neural networks (CNNs) to perform nonlinear regression and predict the mass transport properties with high accuracy. However, in many practical cases, only 2D data is available.